JP2009246437A - Video converting device, video converting method, and video converting program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To convert colors while suppressing the change of the tone of a specified area when converting the colors of video images. <P>SOLUTION: The video image is divided into a plurality of object areas by an area division part 22, the histogram of each area is prepared by a tone maintaining parameter preparation part 23, and a tone maintaining parameter is prepared so as to distribute the histogram of each area in a target distribution range. Further, by preparing a color conversion function using the tone maintaining parameter by an RGB conversion function preparation part 25 and applying the color conversion function to the video image by a video conversion part 27, the tone is maintained in and around the target distribution range in the histogram. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a technique for automatically converting the gradation of an image, and more particularly to a technique for converting the gradation of an input image in a digital image processing apparatus.

  The histogram of the video shows the distribution of light and dark values for each pixel in the video. Contrast stretching is a technique for improving the contrast of an image by widely distributing the light and dark values from a low value to a high value for an image with a skewed light and dark value distribution. Through contrast stretching, an image that is too dark becomes brighter, and an image that is too bright becomes darker to maintain an appropriate brightness value. That is, the overall contrast balance of the video is improved by correcting the luminance value distribution of the video.

  FIG. 16 is a block diagram showing a configuration of a conventional contrast stretching apparatus. As shown in the figure, the conventional contrast stretching apparatus has a distribution calculation unit 42 and a stretching unit 43.

  The distribution calculation unit 42 counts the brightness value from the input video to obtain a histogram value or a histogram function. The target value input unit receives the target minimum value and the target maximum value, and outputs the input values to the stretching unit 43. The stretching unit 43 obtains a light / dark value having a minimum value and a maximum value using the histogram function obtained by the distribution calculation unit 42, and a target value in which the minimum value and the maximum value of the brightness of the input video are input to the target distribution input unit. Transform the histogram to match the lowest and target highest values. In the processing of the stretching unit 43, if the target minimum value is 0 and the target maximum value is 255, the image has brightness values from 0 to 255, and the contrast of the image increases.

Further, the above-described histogram enlarging process is similarly applied to the R component, the G component, and the B component of the video, so that the contrast can be increased while maintaining the color. Alternatively, it is possible to create a video with an increased contrast with little color bias by performing histogram enlargement processing independently on the R component, the G component, and the B component. As prior art documents related to the present application, there are the following.
Japanese Patent Laid-Open No. 4-257082

  However, in the case of contrast stretching according to the prior art, it was possible to improve the overall contrast, but to improve the contrast of other regions while maintaining the contrast of a specific location, for example, a facial region. Processing was impossible.

  The present invention has been made to solve the above-described problems, and the object of the present invention is to convert colors while suppressing a change in gradation in a specific area during color conversion of an image. .

  According to a first aspect of the present invention, there is provided a video conversion device, a storage unit that stores an input video, a region division unit that reads the video, divides it into a plurality of target object regions, and stores the divided regions in a storage unit; Histogram creating means for creating a histogram of each region in the color component, target range designating means for designating a target distribution range for each histogram, and distributing each histogram in the target distribution range A tone maintaining parameter creating means for creating a tone maintaining parameter for maintaining a tone around the distribution range when converting the video, and a color for creating a color conversion function using the tone maintaining parameter Conversion function creating means; and video conversion means for converting the video by applying the color conversion function to the video.

  In the present invention, a video image is divided into a plurality of object regions, a gradation maintaining parameter is created to distribute the histogram of each region in a target distribution range, and a color conversion function is used using the gradation maintaining parameter. And applying the color conversion function to the video, the gradation is maintained around the target distribution range in the histogram.

  In the video conversion apparatus, the area dividing unit detects a face area.

  In the present invention, the gradation of the detected face area is maintained around the target distribution range in the histogram.

  In the video conversion apparatus, the histogram creating means creates a histogram of R component, G component, and B component for each region.

  In the present invention, a gradation maintaining parameter is created for each of the R component, G component, and B component histograms for each divided region, a color conversion function is created using the gradation maintaining parameter, and color conversion is performed. Apply the function to the video to make the color appropriate.

  According to a second aspect of the present invention, there is provided a video conversion method in which a video conversion apparatus stores an input video in a storage unit, reads the video, divides it into a plurality of object areas, and stores the divided video in the storage unit. Creating a histogram of each region in a desired color component, designating a target distribution range for each histogram, and distributing each histogram in the target distribution range Creating a gradation maintaining parameter for maintaining gradation around the distribution range when converting the video, creating a color conversion function using the gradation maintaining parameter, and the color conversion Applying a function to the video to convert the video.

  According to a third aspect of the present invention, there is provided a video conversion program for processing a storage unit to store an input video by a video conversion device, reading the video, dividing it into a plurality of object areas, and storing the division into a storage unit. Processing, processing for creating a histogram of each region in a desired color component, processing for designating a target distribution range for each histogram, and distributing each histogram in the target distribution range A process of creating a gradation maintaining parameter for maintaining gradation around the distribution range when converting the video, a process of creating a color conversion function using the gradation maintaining parameter, and the color conversion A process of applying a function to the video and converting the video is executed by a computer.

  According to the present invention, it is possible to perform conversion while maintaining the gradation of a specific area at the time of color conversion of the image. For example, the brightness and color of the entire image while maintaining the natural gradation of the face area. Can be converted.

  FIG. 1 is a block diagram showing an overall configuration of a video conversion apparatus according to this embodiment. As shown in FIG. 1, the video conversion apparatus includes an input device 1, a central processing control device 2, and a storage device 3. The input device 1 includes a video signal input unit 11, a shutter input unit 12, and a target distribution selection unit 13. The central processing control device 2 includes a frame extraction unit 21, a region division unit 22, a gradation maintenance parameter creation unit 23, The storage device 3 includes a target RGB distribution creation unit 24, an RGB conversion function creation unit 25, a conversion LUT (look-up table) creation unit 26, and a video conversion unit 27. The storage device 3 is a storage unit that stores information output by each unit. , An RGB distribution storage unit 31 and a conversion LUT storage unit 32.

  The video conversion apparatus may be configured as dedicated hardware, or may be configured using a general-purpose computer so that the processing of each unit is executed by a computer program. The results processed by each unit are stored in the storage unit so as to be readable.

  When the video signal is input, the video signal input unit 11 outputs the video signal to the frame extraction unit 21 and the video conversion unit 27. Here, the video output from the video signal input unit 11 is referred to as an input video.

  The input video handled in the present embodiment is a moving image composed of a plurality of frames, and each frame is a still image composed of a plurality of pixels. When the width of the input video is represented by width and the height is represented by height, each frame is composed of width × height pixels. Each pixel is data representing a color in the RGB color model, and is composed of an x coordinate value, a y coordinate value, an R value, a G value, and a B value in the frame. The x coordinate value is an integer value of 1 or more and width or less. Yes, the y-coordinate value is an integer value between 1 and height, and the R value, G value, and B value are integer values between 0 and 255, respectively.

  The target distribution selection unit 13 outputs the target RGB distribution name selected by the user to the target RGB distribution creation unit 24.

  The target RGB distribution creation unit 24 receives the target RGB distribution name and the RGB distribution information group, creates the target RGB distribution information, and outputs the created target RGB distribution information to the gradation maintaining parameter creation unit 23. Note that the RGB distribution information group is input by reading from the RGB distribution storage unit 31.

  When a shutter operation is performed by the user, the shutter input unit 12 outputs a shutter signal that tells the frame extraction unit 21 of the shutter operation.

  The frame extraction unit 21 receives a video signal and a shutter signal and outputs a still image. The still image output from the frame extraction unit 21 is a one-frame still image extracted from the input video.

  The area dividing unit 22 divides the input image into a plurality of object areas.

  The gradation maintenance parameter creation unit 23 creates a histogram of each region in the R component, G component, and B component, and receives the target value output from the target RGB distribution creation unit 24 as an input, and creates a gradation maintenance parameter.

  The RGB conversion function creating unit 25 receives the gradation maintaining parameters, and the R conversion function and G corresponding to each histogram so that the histograms of the R component, the G component, and the B component are in a distribution range that matches the gradation maintaining parameters. A conversion function and a B conversion function are created.

The region dividing unit 22 receives a still image and outputs a region information group. The area information group includes a plurality of area information, and each area information includes an area name R and an area set Region _R. If the x coordinate value of pixel p is xp and the y coordinate value is yp, the region set Region _R is

  It is expressed. The area dividing unit 22 may detect the entire area information, the face area information, and the black eye area information when the subject of the still image is a person. Alternatively, when the subject of the still image is an object, the entire region information, the subject region information, and the background region information may be detected. Hereinafter, an example in which the entire area information, the face area information, and the black eye area information are detected when the subject is a person will be described.

Here, the whole area information is composed of the area name WHOLE and the whole area set Region _WHOLE , the face area set is composed of the area name FACE and the face area set Region _FACE , and the black eye area set is the area name EYE and the black eye area set Region _EYE. It shall be comprised by. The whole region set Region _WHOLE is a set including all pixels included in the input still image.

  FIG. 2 is a block diagram showing a configuration of the area dividing unit 22 for detecting face area information and black eye area information and dividing an input image into a face area and a black eye area. The region dividing unit 22 includes an image scale converting unit 221, a detection target region extracting unit 222, a spatial frequency analyzing unit 223, a face candidate determining unit 224, a face candidate selecting unit 225, a skin region extracting unit 226, And a black eye region extraction unit 227.

  Selection of face candidates in the region dividing unit 22 uses Haar-type feature values, and uses an AdaBoost learning method and a method using a cascade structure detector (see, for example, JP-A-2006-293720).

  The detection target region cutout unit 222 cuts out a region having a predetermined size from the scale-converted image. However, the shape of the region handled here may be an arbitrary shape.

  The spatial frequency analysis unit 223 performs spatial frequency filtering on the cut out region. For example, an AdaBoost learning method and a Haar type feature amount detection method having a cascade detector structure may be used. This method is detailed in P. Viola, M. Jones, "Rapid object Detection using a Boosted Cascade of Simple Features", In Proc. IEEE Conf. On Computer Vision and Pattern Recognition, kauai, USA, 2001. The detailed explanation is omitted.

  The face candidate determination unit 224 determines whether the region can be a face region candidate based on the output value of the spatial frequency analysis unit 223.

  The face candidate selection unit 225 selects and outputs a face area candidate having the maximum area among face area candidates determined to be face candidates.

The skin area extraction unit 226 receives a face area candidate and outputs a face area set Region _FACE . The face area set Region _FACE is composed of pixels included in face area candidates whose pixel color is skin color. In other words, the Region _FACE has xp as the x coordinate value of pixel p, yp as the y coordinate value, rp as the R value, gp as the G value, and bp as the B value.

  It is expressed. Note that the RGB values handled as skin color are set in advance. For example, in the YUV color model, a method is used in which the Cb value is -40 or more and -20 or less and the Cb value is 15 or more and 35 or less as skin color.

The black eye region extraction unit 227 outputs a black eye region set Region _EYE with the face region candidate as an input. The black eye region set Region _EYE is composed of pixels with low luminance values among the pixels included in the face region candidate. More specifically, among the pixels included in the face area candidate, a 1% pixel group is set as a black eye area set in order of increasing luminance value. FIG. 3 is a diagram illustrating an example of processing when the area dividing unit 22 detects a human face.

  The target RGB distribution creation unit 24 receives the target RGB distribution name and the RGB distribution information group and outputs target RGB distribution information. The RGB distribution information group is read from the RGB distribution storage unit 31. The RGB distribution information group includes a plurality of RGB distribution information, and each RGB distribution information has an RGB distribution name and includes a plurality of RGB distribution records. FIG. 4 is a diagram illustrating an example of RGB distribution information. Each RGB distribution record is composed of a record number, a region name, a cumulative ratio value, a gradation maintenance value, a gradation maintenance corresponding record number, and a target color. However, the gradation maintenance value and the gradation maintenance corresponding record number may be included in all RGB distribution records, may include only a part of RGB distribution records, or may not include all RGB distribution records. good. Here, the cumulative ratio value and the gradation maintaining value are decimal values between 0 and 1.

  The color components handled in this embodiment will be described. Luminance (luminance: Y component in the YCbCr color model), Cb (Cb component in the YCbCr color model), Cr (Cr component in the YCbCr color model), black (in the CMYK color model) K component), red (R component in RGB color model), green (G component in RGB color model), blue (B component in RGB color model), hue (H component in HSV color model), saturation (HSV color model) S component) and brightness (V component in the HSV color model), and the minimum value is 0 and the maximum value is 255 in all color components.

  The gradation maintenance parameter creation unit 23 receives a still image, a region information group, and target RGB distribution information, and creates and outputs a gradation maintenance parameter. The gradation maintaining parameter is created by the following procedure.

First, a color value calculation function that returns the color value of the pixel p in the color component Component

And Where g (Component, p, x)

If the pixel p included in the region set Region _R in the pixel set P is extracted, the probability that the color value of the pixel p in the color component Component is x, that is, color (p, Component) = x. Probability density function (histogram function) frequency (P, Region _R , Component, x)

  Is defined.

For example, region information of whole image region information (region name WHOLE, region set Region _WHOLE ), face region information (region name FACE, region set Region _FACE ), and black eye region information (region name EYE, region set Region _EYE ) If the set of pixels representing an image is Py and the luminance color component is Y, the luminance histogram of the entire area is expressed as frequency (Py, Region _WHOLE , Y, x). In addition, the luminance histogram of the face region is represented as frequency (P, Region _FACE , Y, x), and the luminance histogram of the black eye region is represented as frequency (P, Region _EYE , Y, x).

The cumulative distribution function cumulative (P, Region _R , Component, x) derived from the probability density function frequency (P, Region _R , Component, x) representing the histogram of the color component Component is

  It is expressed.

If the probability density function is represented by the curve of FIG. 5, the cumulative distribution function is a curve represented as shown in FIG. Furthermore, the inverse function of the cumulative distribution function with respect to x is

It is expressed. Here, y is a value satisfying the relationship y = cumulative (P, Region _R , Component, x).

Here, the control value calculation function control (P, Region _R , Component, A) is defined as follows using the cumulative distribution function cumulative.

However, here, the set of pixels included in the input still image is P, the region name in the target RGB distribution record is R, the region set corresponding to the region name R created by dividing the still image into regions is Region _R , The color component is Component, and the cumulative ratio value is A.

By applying this control value calculation function to the cumulative ratio value A, as shown in FIG.

  Thus, the control value C corresponding to the cumulative ratio value A is calculated.

Similarly, when the gradation maintenance value is K,

  The value obtained by the above is defined as the gradation maintenance control value L. However, the gradation maintenance control value of the RGB distribution record having no gradation maintenance value is set to the same value as the control value C.

  Using the control value calculation function control, for example, as shown in FIG. 8, a control value and a gradation maintenance control value for a certain color component are calculated corresponding to each RGB distribution record.

  For each RGB distribution record, the target value T can be set by calculating the target color value for a certain color component.

Also, the gradation maintenance control value of the RGB distribution record is L, the target value is T, the control value is C, the gradation maintenance compatible record number is N, and the target value of the RGB distribution record with record number N is the gradation maintenance compatible target. The tone maintenance target value calculation function toneTarget (L, T, T ', C, C') is set as the value T ', the control value is the control value C' corresponding to the tone maintenance

  It is defined as The gradation maintenance target value of each RGB distribution record is calculated as shown in FIG. 8 using this gradation maintenance target value calculation function toneTarget (L, T, T ′, C, C ′). However, the same value as the target value T is set for the gradation maintenance target value of the RGB distribution record that does not have the gradation maintenance value.

For example, when calculating the gradation maintenance target value of RGB distribution record No. 2 in FIG. 8, L = 70, T = 200, T ′ = 220, C = 80, C ′ = 120.

  Thus, the gradation maintenance target value of the RGB distribution record No. 2 is 195. The state of this calculation is shown in FIG. As shown in FIG. 9, when the control value is converted into the target value, C ′ from the control value C is set such that (LC) :( C′−C) = (XT) :( T′−T). X which maintains the ratio of the distance to L and L is calculated as the gradation maintenance target value.

Further, the gradation maintaining control value sequence {L _k } _{k = 1, 2,..., N} (where L1 ≦ L2 ≦ ... ≦ Ln) and the corrected target value sequence {M _k } _{k = 1} corresponding to each control value _{, 2,..., N} , a color conversion function for converting the color value x convert ({L _k } _{k = 1, 2,..., N} , {M _k } _{k = 1, 2,..., N} , x),

  It is defined as

  Here, for example, the luminance histogram of the still image is distributed as shown in FIG. 10, and the gradation maintenance control values {L1, L2, L3, L4} and the gradation maintenance target values {M1, M2, M3, M4}. Is determined as shown in the figure, after converting the luminance value by applying the color conversion function convert ({L1, L2, L3, L4}, {M1, M2, M3, M4}, x) The histogram of is as shown in FIG. As shown in the figure, the color conversion function convert converts the histogram so that the gradation maintenance control value matches the gradation maintenance target value.

Also, using the tone maintenance target value calculation function toneTarget, tone maintenance control value L1, control value C1, target value T1, tone maintenance support control value C2, tone maintenance support target value T2 to tone maintenance target value When M1 is calculated and the gradation maintenance target value M2 is calculated from the gradation maintenance control value L2, the control value C2, the target value T2, the gradation maintenance correspondence control value C1, and the gradation maintenance correspondence target value T1, FIG. As shown, by performing histogram conversion using the color conversion function convert ({L1, L2}, {M1, M2}, x), C1 is converted to T1 and C2 is converted to T2. This means that the gradation between M1 and M2 does not change from the gradation between T1 and T2 even after the histogram conversion. This makes it possible to convert not only between C1 and C2 but also between L1 and L2 with natural gradation.

  Here, as shown in FIG. 13, the R value of the target color is set as the R target value, the G value as the G target value, and the B value as the B target value for each RGB distribution record.

Further, assuming that a set of pixels of a still image is P, an area name of an RGB distribution record Record is AREA, a cumulative ratio value is A, and a gradation maintenance value is K, a control value calculation function control is used to obtain an R control value C _R and R gradation maintenance control value L _R is

  Is calculated.

Furthermore, the R target value of Record is T _R , the record number corresponding to gradation maintenance is N, the RGB distribution record whose record number is N is Record ', the area name of Record' is AREA ', and the cumulative percentage value is Assuming that A ′ and R target value are T _R ′, using the tone maintenance target value calculation function toneTarget, the R tone maintenance target value M _R is

Is calculated. Similarly, the G control value C _G and G gradation maintenance control value L _G of Record are

And the G target value M _G of the record is T _G and the G target value of Record ′ is T _G ′.

Is calculated. In addition, the B control value C _B and B gradation maintenance control value L _B of Record are

Is calculated and the B target value of Record T _B, when 'the B target value of T _B' Record to, the B tone maintains the target value M _B,

  Is calculated.

However, if Record does not have a gradation maintenance value, set as L _R = C _R , M _R = T _R , L _G = C _G , M _G = T _G , L _B = C _B , M _B = T _B To do.

  The R gradation maintenance control value, R gradation maintenance target value, G gradation maintenance control value, G gradation maintenance target value, B gradation maintenance control value, and B gradation maintenance target value are set for each RGB distribution record by the above procedure. FIG. 14 shows a table calculated and arranged in ascending order for the R gradation maintenance control values. The gradation maintenance parameter creation unit 23 outputs the table created here as a gradation maintenance parameter.

  The RGB conversion function creation unit 25 receives the gradation maintaining parameter and outputs an R conversion function, a G conversion function, and a B conversion function.

R gradation maintenance control value corresponding to each record Dk of the gradation maintenance parameter is L _R , K, G gradation maintenance control value is L _G , K, B gradation maintenance control value is L _B , K, R floor The tone maintenance target value is M _R , K, the G gradation maintenance target value is M _G , K, and the B gradation maintenance target value is M _B , K.

Here, the R gradation maintaining control value sequence {L _{R, k} } _{k = 1, 2,..., N} (where L _{R, 1} ≦ L _{R, 2} ≦… ≦ L _{R, n} ) and the R gradation maintaining Target value sequence {M _{R, k} } The R conversion function convert _R (x) that converts R value using _{k = 1,2, ..., n}

G gradation maintaining control value sequence {L _{G, k} } _{k = 1,2, ..., n} (where L _{G, 1} ≤L _{G, 2} ≤ ... ≤L _{G, n} ) and G gradation G conversion function convert _G (x) that converts G value using the maintenance target value sequence {M _{G, k} } _{k = 1,2, ..., n}

B gradation maintaining control value sequence {L _{B, k} } _{k = 1,2, ..., n} (where L _{B, 1} ≤L _{B, 2} ≤ ... ≤L _{B, n} ) and B gradation B conversion function convert _B (x) that converts B value using the maintenance target value sequence {M _{B, k} } _{k = 1,2, ..., n}

  It is defined as

The RGB conversion function creation unit 25 outputs the R conversion function convert _R (x), the G conversion function convert _G (x), and the B conversion function convert _B (x).

  The conversion LUT creation unit 26 receives the R conversion function, the G conversion function, and the B conversion function as inputs, and outputs an R conversion LUT, a G conversion LUT, and a B conversion LUT. Specifically, an LUT as shown in FIG. 15 is created in which R conversion functions, G conversion functions, and B conversion functions are respectively applied to all possible R, G, and B values. And stored in the conversion LUT storage unit 32.

  The video conversion unit 27 receives the input video and the color conversion LUT and outputs a converted video. Specifically, with reference to the color conversion LUT for each pixel of each frame of the input video, the R value, the G value, and the B value are converted to create a converted frame, and the generated converted frame is sequentially output to finally A typical converted video.

  As described above, according to the present embodiment, the image is divided into a plurality of object regions, and a gradation maintaining parameter is created to distribute the histogram of each region in the target distribution range. By creating a color conversion function using the maintenance parameter and applying the color conversion function to the video, the gradation is maintained around the target distribution range in the histogram. That is, as shown in FIG. 12, not only between C1 and C2, but also between L1 and L2 including the periphery thereof is maintained.

  Therefore, according to the present embodiment, it is possible to perform conversion while maintaining the gradation of a specific area at the time of color conversion of the image, for example, the entire image while maintaining the natural gradation of the face area. Brightness and color can be converted.

It is a block diagram which shows the whole structure of the video converter in one Embodiment. It is a block diagram which shows the structure of the area division part 22 for detecting face area information and black eye area information, and dividing an input image | video into a face area and a black eye area. It is a figure which shows an example of the process at the time of detecting a human face in the area | region division part. It is a figure which shows an example of RGB distribution information. It is a figure which shows an example of the probability density function (histogram function) showing the probability that the color value of the pixel p in a certain color component will be x. It is a figure which shows the cumulative distribution function about the probability density function shown in FIG. It is a figure which shows an example of the control value calculation function for calculating a control value from the accumulation ratio value A. FIG. 5 is a diagram showing information obtained by adding control value, gradation maintenance control value, target value, and gradation maintenance target value to each RGB distribution record of FIG. 4. FIG. 6 is a diagram showing a relationship among X, gradation maintenance control value L, target value T, control value C, gradation maintenance compatible target value T ′, and gradation maintenance compatible control value C ′ calculated as gradation maintenance target values. . It is a figure which shows an example of the brightness | luminance histogram of an input image | video. It is a figure which shows the luminance histogram of the image | video after applying a color conversion function to the RGB data of the input image | video used for FIG. It is a figure which shows the histogram before and behind converting by a color conversion function. FIG. 5 is a diagram showing information obtained by adding R target value, G target value, and B target value information to each RGB distribution record of FIG. 4. R gradation maintenance control value, R gradation maintenance target value, G gradation maintenance control value, G gradation maintenance target value, B gradation maintenance control value, B gradation maintenance target value, R gradation maintenance control value It is a figure which shows the table arranged in ascending order. It is a figure which shows an example of the look-up table for each color conversion of R component, G component, and B component. It is a block diagram which shows the structure of the conventional contrast stretching apparatus.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Input device 2 ... Central processing control device 3 ... Memory | storage device 11 ... Video signal input part 12 ... Shutter input part 13 ... Target distribution selection part 21 ... Frame extraction part 22 ... Area division part 23 ... Tone maintenance parameter creation part 24 ... Target RGB distribution creation unit 25 ... RGB conversion function creation unit 26 ... Conversion LUT creation unit 27 ... Video conversion unit 31 ... RGB distribution storage unit 32 ... Conversion LUT storage unit 42 ... Distribution calculation unit 43 ... Stretching unit 221 ... Image scale Conversion unit 222 ... Detection target region extraction unit 223 ... Spatial frequency analysis unit 224 ... Face candidate determination unit 225 ... Face candidate selection unit 226 ... Skin region extraction unit 227 ... Black eye region extraction unit

Claims (5)

Storage means for storing the input video;
An area dividing means for reading out the video and dividing it into areas of a plurality of objects and storing them in a storage means;
A histogram creating means for creating a histogram of each region in a desired color component;
Target range specifying means for specifying a target distribution range for each histogram;
Gradation maintaining parameter creating means for creating a gradation maintaining parameter for maintaining gradation around the distribution range when converting the video so that each histogram is distributed in the target distribution range;
Color conversion function creating means for creating a color conversion function using the gradation maintaining parameter;
Video converting means for converting the video by applying the color conversion function to the video;
A video conversion apparatus comprising:   The video conversion apparatus according to claim 1, wherein the area dividing unit detects a face area.   The video conversion apparatus according to claim 1, wherein the histogram creation unit creates a histogram of R component, G component, and B component for each region. A video conversion method performed by a video conversion device,
Storing the input video in a storage means;
Reading the video, dividing it into a plurality of object areas and storing it in a storage means;
Creating a histogram of each region in a desired color component;
Designating a target distribution range for each of the histograms;
Creating a gradation maintaining parameter for maintaining gradation around the distribution range when converting the video so that each histogram is distributed in the target distribution range;
Creating a color conversion function using the gradation maintaining parameter;
Applying the color conversion function to the video to convert the video;
A video conversion method characterized by comprising:   A video conversion program for causing a computer as the video conversion device according to any one of claims 1 to 3 to execute processing performed by the video conversion device.